In the mobile computing community, most research requires mobile action data in terms of user calling and mobility patterns. Since collection of real data is currently difficult and since most activity data is proprietary, researchers must model this mobile activity data to evaluate their work. Here we model parameterized mobile actions in a wireless personal communication service (PCS) network based on three components: daily movement cycles (traffic and labor), calling patterns, and topological localities. We have also developed a mobile action generator based on our model. Our model is parametrizable to allow for the inclusion of local behavioral data or special cases. For example, by choosing parameters appropriately, our model can emulate the Markov model or the fluid model, both of which have been used extensively in mobile communications research. Under another parameterization, our model produces results in line with simulations based on proprietary real-life data. We have also applied agent-based technology to develop an agent-based call setup protocol and we have demonstrated through simulations how agent-based technology can improve existing call setup protocols.